With so many chip startups targeting the future of deep learning training and inference, one might expect it would be far easier for tech giant Hewlett Packard Enterprise to buy versus build. …
In today’s digital climate, organizations of every size and industry are both collecting and generating enormous amounts of data that can potentially be used to solve the world’s greatest problems—from national security and fraud detection to scientific breakthroughs and technological advancement. …
There are a lot of different ways to skin the deep learning cat. …
One of the luckiest coincidences in the past decade has been that the hybrid machines designed for traditional HPC simulation and modeling workloads. …
Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. …
Having been at the forefront of machine learning since the 1980s when I was a staff scientist in the Theoretical Division at Los Alamos performing basic research on machine learning (and later applying it in many areas including co-founding a machine-learning based drug discovery company), I was lucky enough to participate in the creation and subsequently to observe first-hand the process by which the field of machine-learning grew to become a ‘bandwagon’ that eventually imploded due to misconceptions about the technology and what it could accomplish. …
Few companies have provided better insight into how they think about new hardware for large-scale deep learning than Chinese search giant, Baidu. …
As we previously reported, Google unveiled its second-generation TensorFlow Processing Unit (TPU2) at Google I/O last week. …
Here at The Next Platform, we tend to focus on deep learning as it relates to hardware and systems versus algorithmic innovation, but at times, it is useful to look at the co-evolution of both code and machines over time to see what might be around the next corner. …
It is one thing to scale a neural network on a single GPU or even a single system with four or eight GPUs. …
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